Alexander Varshavsky

Human mobility characterization from cellular network data

Anonymous location data from cellular phone networks sheds light on how people move around on a large scale.

Tapprints: your finger taps have fingerprints

This paper shows that the location of screen taps on modern smartphones and tablets can be identified from accelerometer and gyroscope readings. Our findings have serious implications, as we demonstrate that an attacker can launch a background process on commodity smartphones and tablets, and silently monitor the users inputs, such as keyboard presses and icon taps. While precise tap detection is nontrivial, requiring machine learning algorithms to identify fingerprints of closely spaced keys, sensitive sensors on modern devices aid the process. We present TapPrints, a framework for inferring the location of taps on mobile device touch-screens using motion sensor data combined with machine learning analysis. By running tests on two different off-the-shelf smartphones and a tablet computer we show that identifying tap locations on the screen and inferring English letters could be done with up to 90% and 80% accuracy, respectively. By optimizing the core tap detection capability with additional information, such as contextual priors, we are able to further magnify the core threat.

Human mobility modeling at metropolitan scales

Models of human mobility have broad applicability in fields such as mobile computing, urban planning, and ecology. This paper proposes and evaluates WHERE, a novel approach to modeling how large populations move within different metropolitan areas. WHERE takes as input spatial and temporal probability distributions drawn from empirical data, such as Call Detail Records (CDRs) from a cellular telephone network, and produces synthetic CDRs for a synthetic population. We have validated WHERE against billions of anonymous location samples for hundreds of thousands of phones in the New York and Los Angeles metropolitan areas. We found that WHERE offers significantly higher fidelity than other modeling approaches. For example, daily range of travel statistics fall within one mile of their true values, an improvement of more than 14 times over a Weighted Random Waypoint model. Our modeling techniques and synthetic CDRs can be applied to a wide range of problems while avoiding many of the privacy concerns surrounding real CDRs.

A Tale of One City: Using Cellular Network Data for Urban Planning

Cellular data from call detail records can help urban planners better understand city dynamics. The authors use CDR data to analyze people flow in and out of a suburban city near New York City.

Vis-à-Vis: Privacy-preserving online social networking via Virtual Individual Servers

Online social networks (OSNs) are immensely popular, but their centralized control of user data raises important privacy concerns. This paper presents Vis-à-Vis, a decentralized framework for OSNs based on the privacy-preserving notion of a Virtual Individual Server (VIS). A VIS is a personal virtual machine running in a paid compute utility. In Vis-à-Vis, a person stores her data on her own VIS, which arbitrates access to that data by others. VISs self-organize into overlay networks corresponding to social groups. This paper focuses on preserving the privacy of location information. Vis-à-Vis uses distributed location trees to provide efficient and scalable operations for sharing location information within social groups. We have evaluated our Vis-à-Vis prototype using hundreds of virtual machines running in the Amazon EC2 compute utility. Our results demonstrate that Vis-à-Vis represents an attractive complement to todays centralized OSNs.

Virtual individual servers as privacy-preserving proxies for mobile devices

People increasingly generate content on their mobile devices and upload it to third-party services such as Facebook and Google Latitude for sharing and backup purposes. Although these services are convenient and useful, their use has important privacy implications due to their centralized nature and their acquisitions of rights to user-contributed content. This paper argues that peoples interests would be be better served by uploading their data to a machine that they themselves own and control. We term these machines Virtual Individual Servers (VISs) because our preferred instantiation is a virtual machine running in a highly-available utility computing infrastructure. By using VISs, people can better protect their privacy because they retain ownership of their data and remain in control over the software and policies that determine what data is shared with whom. This paper also describes a range of applications of VIS proxies. It then presents our initial implementation and evaluation of one of these applications, a decentralized framework for mobile social services based on VISs. Our experience so far suggests that building such applications on top of the VIS concept is feasible and desirable.

Obtaining in-context measurements of cellular network performance

Network service providers, and other parties, require an accurate understanding of the performance cellular networks deliver to users. In particular, they often seek a measure of the network performance users experience solely when they are interacting with their device---a measure we call in-context. Acquiring such measures is challenging due to the many factors, including time and physical context, that influence cellular network performance. This paper makes two contributions. First, we conduct a large scale measurement study, based on data collected from a large cellular provider and from hundreds of controlled experiments, to shed light on the issues underlying in-context measurements. Our novel observations show that measurements must be conducted on devices which (i) recently used the network as a result of user interaction with the device, (ii) remain in the same macro-environment (e.g., indoors and stationary), and in some cases the same micro-environment (e.g., in the users hand), during the period between normal usage and a subsequent measurement, and (iii) are currently sending/ receiving little or no user-generated traffic. Second, we design and deploy a prototype active measurement service for Android phones based on these key insights. Our analysis of 1650 measurements gathered from 12 volunteer devices shows that the system is able to obtain average throughput measurements that accurately quantify the performance experienced during times of active device and network usage.

Route classification using cellular handoff patterns

Understanding utilization of city roads is important for urban planners. In this paper, we show how to use handoff patterns from cellular phone networks to identify which routes people take through a city. Specifically, this paper makes three contributions. First, we show that cellular handoff patterns on a given route are stable across a range of conditions and propose a way to measure stability within and between routes using a variant of Earth Movers Distance. Second, we present two accurate classification algorithms for matching cellular handoff patterns to routes: one requires test drives on the routes while the other uses signal strength data collected by high-resolution scanners. Finally, we present an application of our algorithms for measuring relative volumes of traffic on routes leading into and out of a specific city, and validate our methods using statistics published by a state transportation authority.

Ranges of human mobility in Los Angeles and New York

The advent of ubiquitous, mobile, personal devices creates an unprecedented opportunity to improve our understanding of human movement. In this work, we study human mobility in Los Angeles and New York by analyzing anonymous records of approximate locations of cell phones belonging to residents of those cities. We examine two data sets gathered six months apart, each representing hundreds of thousands of people, containing hundreds of millions of location events, and spanning two months of activity. We present, compare, and validate the daily range of travel for people in these populations. Our findings include that human mobility changes with the seasons: both Angelenos and New Yorkers travel less in the winter, with New Yorkers showing a greater decrease in mobility during the cold months. We also show that text messaging activity does not by itself accurately characterize daily range, whereas voice calling alone suffices. Finally, we show that our methodology is accurate by comparing our results to ground truth obtained from volunteers.

Tracking vehicular speed variations by warping mobile phone signal strengths

In this paper, we consider the problem of tracking fine-grained speeds variations of vehicles using signal strength traces from GSM enabled phones. Existing speed estimation techniques using mobile phone signals can provide longer-term speed averages but cannot track short-term speed variations. Understanding short-term speed variations, however, is important in a variety of traffic engineering applications—for example, it may help distinguish slow speeds due to traffic lights from traffic congestion when collecting real time traffic information. Using mobile phones in such applications is particularly attractive because it can be readily obtained from a large number of vehicles. Our approach is founded on the observation that the large-scale path loss and shadow fading components of signal strength readings (signal profile) obtained from the mobile phone on any given road segment appear similar over multiple trips along the same road segment except for distortions along the time axis due to speed variations. We therefore propose a speed tracking technique that uses a Derivative Dynamic Time Warping (DDTW) algorithm to realign a given signal profile with a known training profile from the same road. The speed tracking technique then translates the warping path (i.e., the degree of stretching and compressing needed for alignment) into an estimated speed trace. Using 6.4 hours of GSM signal strength traces collected from a vehicle, we show that our algorithm can estimate vehicular speed with a median error of ± 5mph compared to using a GPS and can capture significant speed variations on road segments with a precision of 68% and a recall of 84%.